Dominik,
There are a number of formulations of this statistic (see the
Kvålseth[*] reference below).
I tend to think of R^2 as the proportion of variance explained by the
model[**]. With the traditional formula, it is possible to get
negative proportions (if there are extreme outliers in the
Hy Max,
thanks again for the answer.
I checked the caret implementation and you were right. If the
predictions for the model constant (or sd(pred)==0) then the
implementation returns a NA for the rSquare (in postResample). This is
mainly because the caret implementation uses `cor` (from the
More information is needed to be sure, but it is most likely that some
of the resampled rpart models produce the same prediction for the
hold-out samples (likely the result of no viable split being found).
Almost every incarnation of R^2 requires the variance of the
prediction. This particular
Thanks Max for your answer.
First, I do not understand your post. Why is it a problem if two of
predictions match? From the formula for calculating R^2 I can see that
there will be a DivByZero iff the total sum of squares is 0. This is
only true if the predictions of all the predicted points from
Sorry for the follow-up, but I dig deeper into the problem.
My text on the R^2 was wrong: In my opinion, and at least to Wikipedia,
R^2 yields a division by zero iff SStot (the total sum of squares) is
zero. SStot is the sum of the sum of the difference between the observed
(not the predicted)
Dominik,
See this line:
Min. 1st Qu. Median Mean 3rd Qu. Max.
30.37 30.37 30.37 30.37 30.37 30.37
The variance of the predictions is zero. caret uses the formula for
R^2 by calculating the correlation between the observed data and the
predictions which uses sd(pred) which
Hy,
I got the following problem when trying to build a rpart model and using
everything but LOOCV. Originally, I wanted to used k-fold partitioning,
but every partitioning except LOOCV throws the following warning:
Warning message: In nominalTrainWorkflow(dat = trainData, info =
trainInfo,
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